Data

The data has been taken from the official portal Federal Office of the Topography swisstopo Rapid Mapping. https://www.rapidmapping.admin.ch/index_de.html.

Resolution:
  Spatial: 10 cm 
  Spectral: 3 bands, RGB
Date:
  Pre - event Data : 19th of May, 2025 
  Event: 28th of May, 2025
  Post - event Data : 30th of May, 2025

Area of Interest:

A small region of the whole area, where the houses have been damaged.

## Loading required package: sp
## terra 1.8.54
pre_img <- rast("AOI//19_5_AOI.tif")
post_img <- rast("AOI//30_5_AOI.tif")

par(mfrow = c(1, 2), 
    oma = c(0, 1, 0, 1)) 
plotRGB(pre_img, axes=FALSE, frame=TRUE)
plotRGB(post_img, axes=FALSE, frame=TRUE)

Applying spatial filters

Laplacian Filter 3 on band 1:
l_kernel <- matrix(c(0,-1,0,
                     -1,3,-1,
                     0,-1,0),
                   nrow = 3, byrow = T)

laplacian_pre_ch_1  <- focal(x = pre_img[[1]], w = l_kernel, fun=sum,  na.policy = "omit", fillvalue = 0)
## |---------|---------|---------|---------|=========================================                                          
laplacian_post_ch_1 <- focal(x = post_img[[1]], w = l_kernel, fun=sum,  na.policy = "omit", fillvalue = 0)
## |---------|---------|---------|---------|=========================================                                          
par(mfrow = c(1, 2),      
    oma = c(1, 1, 1, 1)) 


plot(x=laplacian_pre_ch_1 , col=grey(1:255/255), axes = FALSE, box = TRUE, legend = TRUE)
plot(x=laplacian_post_ch_1, col=grey(1:255/255), axes = FALSE, box = TRUE, legend = TRUE)

Laplacian Filter 3 on band 3:
laplacian_pre_ch_3  <- focal(x = pre_img[[3]], w = l_kernel, fun=sum,  na.policy = "omit", fillvalue = 0)
## |---------|---------|---------|---------|=========================================                                          
laplacian_post_ch_3 <- focal(x = post_img[[3]], w = l_kernel, fun=sum,  na.policy = "omit", fillvalue = 0)
## |---------|---------|---------|---------|=========================================                                          
par(mfrow = c(1, 2),      
    oma = c(1, 1, 1, 1)) 


plot(x=laplacian_pre_ch_3 , col=grey(1:255/255), axes = FALSE, box = TRUE, legend = TRUE)
plot(x=laplacian_post_ch_3, col=grey(1:255/255), axes = FALSE, box = TRUE, legend = TRUE)

GLCM texture analysis
pre_img_raster <- raster(pre_img[[1]])
post_img_raster <- raster(post_img[[1]])

window_size <- c(21, 21)
# all direction
shifts <- list(c(0,1), c(1,0), c(1,1), c(1,-1))

pre_glcm_feature <- glcm(
  x = pre_img_raster,
  window = window_size,
  shift = shifts,
  statistics = "dissimilarity"
)

post_glcm_feature <- glcm(
  x = post_img_raster,
  window = window_size,
  shift = shifts,
  statistics = "dissimilarity"
)
rm(pre_img_raster)
rm(post_img_raster)
# viewing
par(mfrow = c(1, 2),      
    oma = c(1, 1, 1, 1)) 
plot(x=pre_glcm_feature , col=grey(1:255/255), axes = FALSE, box = TRUE, legend = TRUE)
plot(x=post_glcm_feature, col=grey(1:255/255), axes = FALSE, box = TRUE, legend = TRUE)

Stacking Images

# the glcm layers are in raster, 
# converting into spatraster

pre_glcm <- rast(pre_glcm_feature)
post_glcm <- rast(post_glcm_feature)


pre_6_band <- c(pre_img, laplacian_pre_ch_1, laplacian_pre_ch_3, pre_glcm)
post_6_band <- c(post_img, laplacian_post_ch_1, laplacian_post_ch_3, post_glcm)

rm(pre_glcm)
rm(post_glcm)

# viewing
par(mfrow = c(1, 2),      
    oma = c(1, 1, 1, 1)) 
plot(x=pre_6_band , col=grey(1:255/255), axes = FALSE, box = TRUE, legend = TRUE)
plot(x=post_6_band, col=grey(1:255/255), axes = FALSE, box = TRUE, legend = TRUE)

## |---------|---------|---------|---------|=========================================                                          
## |---------|---------|---------|---------|=========================================

Clipping

Clipping the specific region in order to analyse only the village which has been affected by the glacier.

mask <- vect("AOI//final_extent_shape_file//Final_Entent.shp")


pre_crop <- crop(pre_6_band, mask)
## |---------|---------|---------|---------|=========================================                                          
pre_clip <- mask(pre_crop, mask)
## |---------|---------|---------|---------|=========================================                                          
rm(pre_crop)

post_crop <- crop(post_6_band, mask)
## |---------|---------|---------|---------|=========================================                                          
post_clip <- mask(post_crop, mask)
## |---------|---------|---------|---------|=========================================                                          
rm(post_crop)

par(mfrow = c(1, 2),      
    oma = c(0, 1, 0, 1)) 

plotRGB(pre_clip, axes=FALSE, box = TRUE, frame=TRUE)
plotRGB(post_clip, axes=FALSE, box = TRUE, frame=TRUE)

## |---------|---------|---------|---------|=========================================                                          
## |---------|---------|---------|---------|=========================================